Pooling versus model selection for nowcasting with many predictors: an application to German GDP

This paper discusses pooling versus model selection for now- and forecasting in the presence of model uncertainty with large, unbalanced datasets. Empirically, unbalanced data is pervasive in economics and typically due to di¤erent sampling frequencies and publication delays. Two model classes suited in this context are factor models based on large datasets and mixed-data sampling (MIDAS) regressions with few predictors. The specification of these models requires several choices related to, amongst others, the factor estimation method and the number of factors, lag length and indicator selection. Thus, there are many sources of mis-specification when selecting a particular model, and an alternative could be pooling over a large set of models with different specifications. We evaluate the relative performance of pooling and model selection for now- and forecasting quarterly German GDP, a key macroeconomic indicator for the largest country in the euro area, with a large set of about one hundred monthly indicators. Our empirical findings provide strong support for pooling over many specifications rather than selecting a specific model. --casting,forecast combination,forecast pooling,model selection,mixed - frequency data,factor models,MIDAS

MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area

This paper compares the mixed-data sampling (MIDAS) and mixed-frequency VAR (MF-VAR) approaches to model speci…cation in the presence of mixed-frequency data, e.g., monthly and quarterly series. MIDAS leads to parsimonious models based on exponential lag polynomials for the coe¢ cients, whereas MF-VAR does not restrict the dynamics and therefore can su¤er from the curse of dimensionality. But if the restrictions imposed by MIDAS are too stringent, the MF-VAR can perform better. Hence, it is di¢ cult to rank MIDAS and MF-VAR a priori, and their relative ranking is better evaluated empirically. In this paper, we compare their performance in a relevant case for policy making, i.e., nowcasting and forecasting quarterly GDP growth in the euro area, on a monthly basis and using a set of 20 monthly indicators. It turns out that the two approaches are more complementary than substitutes, since MF-VAR tends to perform better for longer horizons, whereas MIDAS for shorter horizons. --nowcasting,mixed-frequency data,mixed-frequency VAR,MIDAS

Genome size, cytogenetic data and transferability of EST-SSRs markers in wild and cultivated species of the genus Theobroma L. (Byttnerioideae, Malvaceae).

The genus Theobroma comprises several trees species native to the Amazon. Theobroma cacao L. plays a key economic role mainly in the chocolate industry. Both cultivated and wild forms are described within the genus. Variations in genome size and chromosome number have been used for prediction purposes including the frequency of interspecific hybridization or inference about evolutionary relationships. In this study, the nuclear DNA content, karyotype and genetic diversity using functional microsatellites (EST-SSR) of seven Theobroma species were characterized. The nuclear content of DNA for all analyzed Theobroma species was 1C = ~ 0.46 pg. These species presented 2n = 20 with small chromosomes and only one pair of terminal heterochromatic bands positively stained (CMA+/DAPI− bands). The small size of Theobroma ssp. genomes was equivalent to other Byttnerioideae species, suggesting that the basal lineage of Malvaceae have smaller genomes and that there was an expansion of 2C values in the more specialized family clades. A set of 20 EST-SSR primers were characterized for related species of Theobroma, in which 12 loci were polymorphic. The polymorphism information content (PIC) ranged from 0.23 to 0.65, indicating a high level of information per locus. Combined results of flow cytometry, cytogenetic data and EST-SSRs markers will contribute to better describe the species and infer about the evolutionary relationships among Theobroma species. In addition, the importance of a core collection for conservation purposes is highlighted

How to increase technology transfers to developing countries: a synthesis of the evidence

The existing United Nations Framework Convention on Climate Change (UNFCCC) has failed to deliver the rate of low-carbon technology transfer (TT) required to curb GHG emissions in developing countries. This failure has exposed the limitations of universalism and renewed interest in bilateral approaches to TT. Gaps are identified in the UNFCCC approach to climate change TT: missing links between international institutions and the national enabling environments that encourage private investment; a non-differentiated approach for (developing) country and technology characteristics; and a lack of clear measurements of the volume and effectiveness of TTs. Evidence from econometric literature and business experience on climate change TT is reviewed, so as to address the identified pitfalls of the UNFCCC process. Strengths and weaknesses of different methodological approaches are highlighted. International policy recommendations are offered aimed at improving the level of emission reductions achieved through TT